In SUS, the user selected at i th iteration π S (i) is the one with the largest channel norm on the null space of the subspace spanned by the channel vectors of i - 1 earlier selected users.
Different to[10], the precoding matrix at the relay is constructed by projecting each aligned message on a single carefully chosen direction of the null space, respectively, where the inverse of large size matrices can be avoided.
Because the sample number in training set is larger than the dimension of feature, there was no null space for within scatter matrix Sw.
While mathematically rigorous, enumerating null space vector combinations can become intractable for large-scale problems, similar to EFM analysis.
We describe an implicitly restarted block Lanczos method designed for the computation of a few extreme multiple or close eigenvalues and associated eigenvectors of a large sparse symmetric matrix and apply this method to determine the desired null space.
The path of the minimal energy configuration at a bifurcation point can be computed from the null space of a sparse symmetric matrix, which typically is very large, e.g., of order 3 × 105.
Project all unprocessed channels onto the null space of A. Select the user, π A (i), with largest 2-norm projection and label it processed.
Project all unprocessed channels onto the null space of S. Select the user, π S (i), with largest 2-norm projection and label it processed.
From i = 2 to K s (3) Project all unprocessed channels onto the null space of A. (4) Select the user, π A (i), with largest 2-norm projection and label it processed.
From i = 1 to K s (2) Project all unprocessed channels onto the null space of S. (3) Select the user, π S (i), with largest 2-norm projection and label it processed.
